We modified the MNIST dataset [1]} to simulate temporal and longitudinal effects.
To simulate heterogeneity and the effect of different observation likelihoods, we divided each image into 4 regions, \(\mathcal {R}_1, \dots , \mathcal {R}_4\)  and created five different datasets, \(\mathcal {D}_1, \dots , \mathcal {D}_5\) , as shown in fig:4Qsetup.
Additionally, we masked 25% of the pixels during the training and performed 5 independent runs with each dataset.
